In digital cameras a lens is used to obtain light information from a scene. These light photons are converted to electrons by an image sensor consisting of a number of light sensitive elements. When using digital RGB still cameras, three image sensors are used to obtain color sensing for each color red, green and blue. In order to reduce both cost, weight and size of the camera it is possible to use one image sensor having an RGB Bayer filter array, where each pixel in the sensor array senses red, green or blue in a predefined pattern. This pattern is build up of alternating green, red columns and green, blue columns.
In FIG. 1 the color filter array of an RGB Bayer image sensor is shown. It is assumed that the sensor has square and contiguous pixels, this means that the resolution has become independent of the aspect ratio of the sensor and is equal for the horizontal and vertical direction. The sample structure of the RGB pixels is shown in FIG. 2. The pitch p is the distance between two neighbor pixels. Its inverse value represents the pixel or system clock frequency ‘fs’ in case of a single RGB output of the sensor, so fs=1/p. The sample frequency of each RGB color is inversely proportional to the shortest distance between the pixels of each RGB color. This results for the red and blue colors in a horizontal and vertical sample frequency called fsR and fsB, equal to ½ p=fs/2, and for green in a diagonal sample frequency fsG equal to 1/p√2=fs/√2. Because fsR and fsB are equal this frequency is called fsRB.
For the sampled image it holds that the frequency spectrum of each color is repeated at multiples of the sample frequency of each color. The multiple sample frequencies are located on equal distances in a two-dimensional array. By means of the black dots in FIG. 3, the left-hand part of FIG. 3 showing a red and blue frequency array, and the right-hand part of FIG. 3 showing a green sample frequency array, a very limited number of multiple sample frequency points are shown. The grey areas N are Nyquist areas. Theoretically that number is infinite. If the frequency spectrums overlap aliasing is introduced. If each spectrum around a sample frequency point does not overlap the so called Nyquist theorem is fulfilled: i.e. the incoming frequencies of the scene, via the optical system and the integrating light sensitive part of a sensor pixel, should be lower than half the sample frequency of each color. This means that the maximum resolution that each color can offer is determined by half the sample frequency of each color. So for the red and blue colored pixels the maximum resolution in the horizontal and vertical direction is fsRB/2=fs/4, and in the diagonal direction √2*fs/4. For the green pixels the diagonal resolution has a maximum of fsG/2=fs/2√2 and due to the quincunx structure in the horizontal and vertical direction of fs/2.
Contour filtering is performed in order to obtain a contour signal that can be used to get a sharper image by enhancing the contour of the image. It is important that the contour filter does not interpret different RGB amplitudes as contours. Meaning that when filtering colored scene parts, having different RGB amplitudes, the output should be zeros.
When in a digital still camera a contour filter is applied a disadvantage is that the back folding aliasing artefacts described above will be enhanced, making those artefacts even more visible.